Initialization

Libraries

library(BSgenome.Mmusculus.UCSC.mm10)
library(plyranges)
library(tidyverse)
library(magrittr)
library(cowplot)

Parameters

EPSILON = 30
TPM = 5
WIN_SIZE = 1000
RADIUS = 300
N_CTS_LOW = 10
N_CTS_HIGH = 80

COLOR_VALS_CPA=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D")
COLOR_VALS_CPA_PA=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D", AAAAAA="#888888")
COLOR_VALS_EXTRA=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D", CAATTA="orchid4")

FILE_UTROME = sprintf("data/granges/utrome_gr_txs.e%d.t%d.gc25.pas3.f0.9999.w500.Rds", EPSILON, TPM)
FILE_BED = sprintf("data/bed/celltypes/celltypes.e%d.t%d.bed.gz", EPSILON, TPM)
FILE_BED_UNLIKELY = sprintf("data/bed/cleavage-sites/utrome.unlikely.e%d.t%d.gc25.pas3.f0.9999.bed.gz",
                            EPSILON, TPM)

Functions

get_centered_motif_sites <- function (motif, seqs) {
  seqs %>% 
    vmatchPattern(pattern=motif, fixed=FALSE) %>%
    unlist %>% as.data.frame %>%
    mutate(motif=motif,
           start=start - WIN_SIZE/2,
           end=end - WIN_SIZE/2,
           center=(end + start)/2) %>%
    select(motif, center, start, end)
}

## plot density from motif positions data.frame
plot_motif_density <- function (df_motifs, radius=RADIUS, title="UTRome",
                                col_values=COLOR_VALS_CPA) {
  df_motifs %>%
    ggplot(aes(x=center, color=motif)) +
    stat_density(aes(y=..scaled..), geom='line', position='identity', 
                 size=1.5, alpha=0.9) +
    geom_hline(yintercept=0) +
    geom_vline(xintercept=0, linetype='dashed', color='black') +
    coord_cartesian(xlim=c(-radius, radius)) +
    scale_x_continuous(breaks=seq(-radius, radius, 100), 
                       limits=c(-radius - 100, radius+100)) +
    scale_color_manual(values=col_values) +
    labs(x=sprintf("Distance from Cleavage Site (%s)", title),
         y="Relative Density", color="Motif") +
    guides(color=guide_legend(override.aes=list(alpha=1, size=3))) +
    theme_minimal_vgrid()
}

Load Data

Cleavage Sites by Cell Type

gr_sites <- read_bed(FILE_BED) %>% 
    `seqlevelsStyle<-`("UCSC") %>%
    keepStandardChromosomes(pruning.mode="coarse") %>%
    anchor_center() %>%
    mutate(width=EPSILON)

Mouse UTRome Transcripts

## Load all transcripts
gr_txs <- readRDS(FILE_UTROME) %>% anchor_3p()

## focus on cleavage sites
gr_cleavage <- gr_txs %>%
    mutate(n_celltypes=count_overlaps_directed(mutate(., width=0), gr_sites)) %>%
    mutate(width=WIN_SIZE) %>%
    shift_downstream(WIN_SIZE/2) %>%
    mutate(origin=ifelse(is_novel, "UTRome", "GENCODE"),
           origin_ud=case_when(
               !is_novel ~ "GENCODE",
               str_detect(transcript_id, "UTR-") ~ "upstream",
               str_detect(transcript_id, "UTR+") ~ "downstream"
           ))

Cleavage Sites Marked as Internal Priming

gr_ip <- read_bed(FILE_BED_UNLIKELY) %>%
  anchor_3p %>%
  mutate(width=WIN_SIZE) %>%
  shift_downstream(WIN_SIZE/2)

Subgroups

gr_single <- filter(gr_cleavage, utr_type == "single")
gr_ipa <- filter(gr_cleavage, is_ipa)
gr_multi_tandem <- filter(gr_cleavage, utr_type == 'multi', !is_ipa)

gr_gencode <- filter(gr_cleavage, !is_novel)
gr_novel <- filter(gr_cleavage, is_novel)

gr_proximal_gc <- filter(gr_cleavage, utr_type == 'multi', is_proximal, !is_novel)
gr_nondistal_gc <- filter(gr_cleavage, utr_type == 'multi', !is_distal, !is_novel)
gr_distal_gc <- filter(gr_cleavage, utr_type == 'multi', is_distal, !is_novel)

gr_proximal_novel <- filter(gr_cleavage, utr_type == 'multi', is_proximal, is_novel)
gr_nondistal_novel <- filter(gr_cleavage, utr_type == 'multi', !is_distal, is_novel)
gr_distal_novel <- filter(gr_cleavage, utr_type == 'multi', is_distal, is_novel)

## only GENCODE
gr_ctnone_gc <- filter(gr_cleavage, !is_novel, n_celltypes == 0)
gr_common <- filter(gr_cleavage, !is_novel, n_celltypes > 0)

gr_ctlow_gc <- filter(gr_cleavage, !is_novel, n_celltypes > 0, n_celltypes < N_CTS_LOW)
gr_ctmid_gc <- filter(gr_cleavage, !is_novel, n_celltypes >= N_CTS_LOW, n_celltypes < N_CTS_HIGH)
gr_cthigh_gc <- filter(gr_cleavage, !is_novel, n_celltypes >= N_CTS_HIGH)

gr_ctlow_novel <- filter(gr_cleavage, is_novel, n_celltypes < N_CTS_LOW)
gr_ctmid_novel <- filter(gr_cleavage, is_novel, n_celltypes >= N_CTS_LOW, n_celltypes < N_CTS_HIGH)
gr_cthigh_novel <- filter(gr_cleavage, is_novel, n_celltypes >= N_CTS_HIGH)

Plots

All Sites

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_cleavage)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="All")

Single-UTR Genes

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_single)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Single UTR Genes")

Intronic Cleavage Sites

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ipa)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Intronic Sites")

Tandem Cleavage Sites

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_multi_tandem)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Tandem Sites")

GENCODE

All Cleavage Sites (42588 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_gencode)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE Sites")

Proximal Cleavage Sites (9764 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_proximal_gc)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE Proximal Sites")

Non-Distal Cleavage Sites (23993 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_nondistal_gc)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE Non-Distal Sites")

Distal Cleavage Sites (13020 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_distal_gc)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE Distal Sites")

Cell Types High (4744 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_cthigh_gc)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE - Cell Types High")

Cell Types Midrange (10555 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ctmid_gc)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE - Cell Types Midrange")

Cell Types Low (10064 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ctlow_gc)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE - Cell Types Low")

No Cell Types Supporting (17225 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ctnone_gc)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE - No Supporting Cell Types")

Export

ggsave("img/sq/sup1c-motif-density-gencode-mca.pdf",
       width=8, height=6, dpi=300)
## Warning: Removed 50718 rows containing non-finite values (`stat_density()`).

Common Cleavage Sites (25363 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_common)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="GENCODE + MCA")

Export

ggsave("img/sq/sup1c-motif-density-common-mca.pdf",
       width=8, height=6, dpi=300)
## Warning: Removed 68837 rows containing non-finite values (`stat_density()`).

Novel Cleavage Sites

All Cleavage Sites (19936 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_novel)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Novel Sites")

Export

ggsave("img/sq/sup1c-motif-density-utrome-mca.pdf",
       width=8, height=6, dpi=300)
## Warning: Removed 62374 rows containing non-finite values (`stat_density()`).

Proximal Cleavage Sites (6047 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_proximal_novel)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Novel Proximal Sites")

Non-Distal Cleavage Sites (17145 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_nondistal_novel)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Novel Non-Distal Sites")

Distal Cleavage Sites (2791 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_distal_novel)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Novel Distal Sites")

Cell Types High (1326 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_cthigh_novel)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Novel - Cell Types High")

Cell Types Midrange (4602 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ctmid_novel)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Novel - Cell Types Midrange")

Cell Types Low (14008 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ctlow_novel)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Novel - Cell Types Low")

Internal Priming Sites (89549 sites)

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ip)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)

rbind(df_tgta, df_pas, df_tktktk) %>%
  plot_motif_density(title="Internal Priming Sites")

seqs <- getSeq(BSgenome.Mmusculus.UCSC.mm10, gr_ip)

df_tgta <- get_centered_motif_sites("TGTA", seqs)
df_pas <- get_centered_motif_sites(motif="AWTAAA", seqs)
df_tktktk <- get_centered_motif_sites(motif="TKTKTK", seqs)
df_aaaaaa <- get_centered_motif_sites(motif="AAAAAA", seqs)

rbind(df_tgta, df_pas, df_tktktk, df_aaaaaa) %>%
  plot_motif_density(title="Internal Priming Sites", col_values=COLOR_VALS_CPA_PA)

Combined Motif Analysis

UTRome vs GENCODE

grs_cleavage <- gr_cleavage %>% split(.$origin)

seqs_cleavage <- lapply(grs_cleavage, . %>% getSeq(x=BSgenome.Mmusculus.UCSC.mm10))

lapply_get_centered_motif_sites <- function(motif, seqs=seqs_cleavage) {
  lapply(seqs, . %>% get_centered_motif_sites(motif=motif)) %>% 
    do.call(what=rbind) %>%
    mutate(origin=str_extract(rownames(.), "^[^.]+")) %>%
    `rownames<-`(NULL)
}

df_tgta <- lapply_get_centered_motif_sites(motif="TGTA") %>%
  mutate(origin=factor(origin, levels=c("GENCODE", "UTRome")))
df_pas <- lapply_get_centered_motif_sites(motif="AWTAAA") %>%
  mutate(origin=factor(origin, levels=c("GENCODE", "UTRome")))
df_tktktk <- lapply_get_centered_motif_sites(motif="TKTKTK") %>%
  mutate(origin=factor(origin, levels=c("GENCODE", "UTRome")))

df_combined <- rbind(df_tgta, df_pas, df_tktktk)

Plot

df_combined %>%
  ggplot(aes(x=center, color=motif, linetype=origin)) +
  stat_density(aes(y=..scaled..), geom='line', position='identity', 
               size=1.0, alpha=0.9) +
  geom_hline(yintercept=0) +
  geom_vline(xintercept=0, linetype='dashed', color='black') +
  coord_cartesian(xlim=c(-RADIUS, RADIUS)) +
  scale_x_continuous(breaks=seq(-RADIUS, RADIUS, 100), 
                     limits=c(-RADIUS - 100, RADIUS+100)) +
  scale_color_manual(values=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D")) +
  scale_linetype_manual(values=c("GENCODE"="solid", "UTRome"="dashed")) +
  labs(x="Distance from RefSeq Cleavage Site",
       y="Relative Density", color="Motif", linetype="Annotation") +
  guides(color=guide_legend(override.aes=list(alpha=1, size=3))) +
  theme_minimal_vgrid()

GENCODE vs Upstream vs Downstream

grs_cleavage_ud <- gr_cleavage %>% split(.$origin_ud)

seqs_cleavage_ud <- lapply(grs_cleavage_ud, . %>% getSeq(x=BSgenome.Mmusculus.UCSC.mm10))

lapply_get_centered_motif_sites <- function(motif, seqs=seqs_cleavage_ud) {
  lapply(seqs, . %>% get_centered_motif_sites(motif=motif)) %>% 
    do.call(what=rbind) %>%
    mutate(origin=str_extract(rownames(.), "^[^.]+")) %>%
    `rownames<-`(NULL)
}

df_tgta_ud <- lapply_get_centered_motif_sites(motif="TGTA") %>%
  mutate(origin=factor(origin, levels=c("GENCODE", "upstream", "downstream")))
df_pas_ud <- lapply_get_centered_motif_sites(motif="AWTAAA") %>%
  mutate(origin=factor(origin, levels=c("GENCODE", "upstream", "downstream")))
df_tktktk_ud <- lapply_get_centered_motif_sites(motif="TKTKTK") %>%
  mutate(origin=factor(origin, levels=c("GENCODE", "upstream", "downstream")))

df_combined_ud <- rbind(df_tgta_ud, df_pas_ud, df_tktktk_ud)

Plot All

df_combined_ud %>%
  ggplot(aes(x=center, color=motif, linetype=origin)) +
  stat_density(aes(y=..scaled..), geom='line', position='identity', 
               size=1.0, alpha=0.9) +
  geom_hline(yintercept=0) +
  geom_vline(xintercept=0, linetype='dashed', color='black') +
  coord_cartesian(xlim=c(-RADIUS, RADIUS)) +
  scale_x_continuous(breaks=seq(-RADIUS, RADIUS, 100), 
                     limits=c(-RADIUS - 100, RADIUS+100)) +
  scale_color_manual(values=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D")) +
  scale_linetype_manual(values=c("GENCODE"="solid", "upstream"="dotted", "downstream"="dashed")) +
  labs(x="Distance from RefSeq Cleavage Site",
       y="Relative Density", color="Motif", linetype="Annotation") +
  guides(color=guide_legend(override.aes=list(alpha=1, size=3))) +
  theme_minimal_vgrid()

Plot Individual

TGTA

df_tgta_ud %>%
  ggplot(aes(x=center, color=motif, linetype=origin)) +
  stat_density(aes(y=..scaled..), geom='line', position='identity', 
               size=1.0, alpha=0.9) +
  geom_hline(yintercept=0) +
  geom_vline(xintercept=0, linetype='dashed', color='black') +
  coord_cartesian(xlim=c(-RADIUS, RADIUS)) +
  scale_x_continuous(breaks=seq(-RADIUS, RADIUS, 100), 
                     limits=c(-RADIUS - 100, RADIUS+100)) +
  scale_color_manual(values=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D")) +
  scale_linetype_manual(values=c("GENCODE"="solid", "upstream"="dotted", "downstream"="dashed")) +
  labs(x="Distance from RefSeq Cleavage Site",
       y="Relative Density", color="Motif", linetype="Annotation") +
  guides(color=FALSE) +
  theme_minimal_vgrid()

AWTAAA

df_pas_ud %>%
  ggplot(aes(x=center, color=motif, linetype=origin)) +
  stat_density(aes(y=..scaled..), geom='line', position='identity', 
               size=1.0, alpha=0.9) +
  geom_hline(yintercept=0) +
  geom_vline(xintercept=0, linetype='dashed', color='black') +
  coord_cartesian(xlim=c(-RADIUS, RADIUS)) +
  scale_x_continuous(breaks=seq(-RADIUS, RADIUS, 100), 
                     limits=c(-RADIUS - 100, RADIUS+100)) +
  scale_color_manual(values=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D")) +
  scale_linetype_manual(values=c("GENCODE"="solid", "upstream"="dotted", "downstream"="dashed")) +
  labs(x="Distance from RefSeq Cleavage Site",
       y="Relative Density", color="Motif", linetype="Annotation") +
  guides(color=FALSE) +
  theme_minimal_vgrid()

TKTKTK

df_tktktk_ud %>%
  ggplot(aes(x=center, color=motif, linetype=origin)) +
  stat_density(aes(y=..scaled..), geom='line', position='identity', 
               size=1.0, alpha=0.9) +
  geom_hline(yintercept=0) +
  geom_vline(xintercept=0, linetype='dashed', color='black') +
  coord_cartesian(xlim=c(-RADIUS, RADIUS)) +
  scale_x_continuous(breaks=seq(-RADIUS, RADIUS, 100), 
                     limits=c(-RADIUS - 100, RADIUS+100)) +
  scale_color_manual(values=c(TGTA="#73ADD6", AWTAAA="#358C44", TKTKTK="#F36B4D")) +
  scale_linetype_manual(values=c("GENCODE"="solid", "upstream"="dotted", "downstream"="dashed")) +
  labs(x="Distance from RefSeq Cleavage Site",
       y="Relative Density", color="Motif", linetype="Annotation") +
  guides(color=FALSE) +
  theme_minimal_vgrid()


Runtime Details

Session Info

## R version 4.2.2 (2022-10-31)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_16/lib/libopenblasp-r0.3.21.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] cowplot_1.1.1                      magrittr_2.0.3                    
##  [3] lubridate_1.9.2                    forcats_1.0.0                     
##  [5] stringr_1.5.0                      dplyr_1.1.0                       
##  [7] purrr_1.0.1                        readr_2.1.4                       
##  [9] tidyr_1.3.0                        tibble_3.1.8                      
## [11] ggplot2_3.4.1                      tidyverse_2.0.0                   
## [13] plyranges_1.18.0                   BSgenome.Mmusculus.UCSC.mm10_1.4.3
## [15] BSgenome_1.66.3                    rtracklayer_1.58.0                
## [17] Biostrings_2.66.0                  XVector_0.38.0                    
## [19] GenomicRanges_1.50.0               GenomeInfoDb_1.34.9               
## [21] IRanges_2.32.0                     S4Vectors_0.36.0                  
## [23] BiocGenerics_0.44.0               
## 
## loaded via a namespace (and not attached):
##  [1] MatrixGenerics_1.10.0       Biobase_2.58.0             
##  [3] sass_0.4.2                  jsonlite_1.8.4             
##  [5] bslib_0.4.1                 highr_0.9                  
##  [7] GenomeInfoDbData_1.2.9      Rsamtools_2.14.0           
##  [9] yaml_2.3.6                  pillar_1.8.1               
## [11] lattice_0.20-45             glue_1.6.2                 
## [13] digest_0.6.30               colorspace_2.0-3           
## [15] htmltools_0.5.4             Matrix_1.5-3               
## [17] XML_3.99-0.12               pkgconfig_2.0.3            
## [19] zlibbioc_1.44.0             scales_1.2.1               
## [21] tzdb_0.3.0                  BiocParallel_1.32.0        
## [23] timechange_0.2.0            farver_2.1.1               
## [25] generics_0.1.3              ellipsis_0.3.2             
## [27] cachem_1.0.6                withr_2.5.0                
## [29] SummarizedExperiment_1.28.0 cli_3.6.0                  
## [31] crayon_1.5.2                evaluate_0.18              
## [33] fansi_1.0.3                 textshaping_0.3.6          
## [35] tools_4.2.2                 hms_1.1.2                  
## [37] BiocIO_1.8.0                lifecycle_1.0.3            
## [39] matrixStats_0.62.0          munsell_0.5.0              
## [41] DelayedArray_0.24.0         compiler_4.2.2             
## [43] jquerylib_0.1.4             systemfonts_1.0.4          
## [45] rlang_1.1.0                 grid_4.2.2                 
## [47] RCurl_1.98-1.9              rstudioapi_0.14            
## [49] rjson_0.2.21                labeling_0.4.2             
## [51] bitops_1.0-7                rmarkdown_2.18             
## [53] restfulr_0.0.15             gtable_0.3.1               
## [55] codetools_0.2-18            R6_2.5.1                   
## [57] GenomicAlignments_1.34.0    knitr_1.40                 
## [59] fastmap_1.1.0               utf8_1.2.2                 
## [61] ragg_1.2.5                  stringi_1.7.8              
## [63] parallel_4.2.2              vctrs_0.6.1                
## [65] tidyselect_1.2.0            xfun_0.34

Conda Environment

## Conda Environment YAML
name: bioc_3_16
channels:
  - merv
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - _r-mutex=1.0.1=anacondar_1
  - argcomplete=2.0.0=pyhd8ed1ab_0
  - bioconductor-annotate=1.76.0=r42hdfd78af_0
  - bioconductor-annotationdbi=1.60.0=r42hdfd78af_0
  - bioconductor-annotationfilter=1.22.0=r42hdfd78af_0
  - bioconductor-annotationhub=3.6.0=r42hdfd78af_0
  - bioconductor-apeglm=1.20.0=r42hb890f52_0
  - bioconductor-batchelor=1.14.0=r42hb890f52_0
  - bioconductor-beachmat=2.14.0=r42hb890f52_0
  - bioconductor-biobase=2.58.0=r42h3be46a4_0
  - bioconductor-biocfilecache=2.6.0=r42hdfd78af_0
  - bioconductor-biocgenerics=0.44.0=r42hdfd78af_0
  - bioconductor-biocio=1.8.0=r42hdfd78af_0
  - bioconductor-biocneighbors=1.16.0=r42hb890f52_0
  - bioconductor-biocparallel=1.32.0=r42hb890f52_0
  - bioconductor-biocsingular=1.14.0=r42hb890f52_0
  - bioconductor-biocversion=3.16.0=r42hdfd78af_0
  - bioconductor-biomart=2.54.0=r42hdfd78af_0
  - bioconductor-biostrings=2.66.0=r42h3be46a4_0
  - bioconductor-biovizbase=1.46.0=r42h3be46a4_0
  - bioconductor-bluster=1.8.0=r42hb890f52_0
  - bioconductor-bsgenome=1.66.3=r42hdfd78af_0
  - bioconductor-bsgenome.hsapiens.ucsc.hg38=1.4.5=r42hdfd78af_0
  - bioconductor-bsgenome.mmusculus.ucsc.mm10=1.4.3=r42hdfd78af_2
  - bioconductor-celldex=1.8.0=r42hdfd78af_0
  - bioconductor-cellxgenedp=1.2.0=r42hdfd78af_0
  - bioconductor-clusterprofiler=4.6.0=r42hdfd78af_0
  - bioconductor-cner=1.34.0=r42h3be46a4_0
  - bioconductor-data-packages=20230420=hdfd78af_0
  - bioconductor-delayedarray=0.24.0=r42h3be46a4_0
  - bioconductor-delayedmatrixstats=1.20.0=r42hdfd78af_0
  - bioconductor-deseq2=1.38.0=r42hb890f52_0
  - bioconductor-dirichletmultinomial=1.40.0=r42ha5537d9_0
  - bioconductor-dose=3.24.0=r42hdfd78af_0
  - bioconductor-dropletutils=1.18.0=r42hb890f52_0
  - bioconductor-edger=3.40.0=r42hb890f52_0
  - bioconductor-enhancedvolcano=1.16.0=r42hdfd78af_0
  - bioconductor-enrichplot=1.18.0=r42hdfd78af_0
  - bioconductor-ensdb.hsapiens.v86=2.99.0=r42hdfd78af_10
  - bioconductor-ensembldb=2.22.0=r42hdfd78af_0
  - bioconductor-experimenthub=2.6.0=r42hdfd78af_0
  - bioconductor-fgsea=1.24.0=r42hb890f52_0
  - bioconductor-genefilter=1.80.0=r42h238a2e4_0
  - bioconductor-genelendatabase=1.34.0=r42hdfd78af_0
  - bioconductor-geneplotter=1.76.0=r42hdfd78af_0
  - bioconductor-genomeinfodb=1.34.9=r42hdfd78af_0
  - bioconductor-genomeinfodbdata=1.2.9=r42hdfd78af_0
  - bioconductor-genomicalignments=1.34.0=r42h3be46a4_0
  - bioconductor-genomicfeatures=1.50.2=r42hdfd78af_0
  - bioconductor-genomicranges=1.50.0=r42h3be46a4_0
  - bioconductor-genomicscores=2.10.0=r42hdfd78af_0
  - bioconductor-ggtree=3.6.0=r42hdfd78af_0
  - bioconductor-go.db=3.16.0=r42hdfd78af_0
  - bioconductor-gosemsim=2.24.0=r42hb890f52_0
  - bioconductor-goseq=1.50.0=r42hdfd78af_0
  - bioconductor-gviz=1.42.0=r42hdfd78af_0
  - bioconductor-hcadata=1.14.0=r42hdfd78af_0
  - bioconductor-hdf5array=1.26.0=r42h6e925bd_1
  - bioconductor-hdo.db=0.99.1=r42hdfd78af_0
  - bioconductor-ihw=1.26.0=r42hdfd78af_0
  - bioconductor-interactionset=1.26.0=r42hb890f52_0
  - bioconductor-interactivedisplaybase=1.36.0=r42hdfd78af_0
  - bioconductor-iranges=2.32.0=r42h3be46a4_0
  - bioconductor-keggrest=1.38.0=r42hdfd78af_0
  - bioconductor-limma=3.54.0=r42h3be46a4_0
  - bioconductor-lpsymphony=1.26.0=r42hb890f52_0
  - bioconductor-m3drop=1.24.0=r42hdfd78af_0
  - bioconductor-matrixgenerics=1.10.0=r42hdfd78af_0
  - bioconductor-metapod=1.6.0=r42hb890f52_0
  - bioconductor-motifstack=1.42.0=r42hdfd78af_0
  - bioconductor-nullranges=1.4.0=r42hdfd78af_0
  - bioconductor-org.hs.eg.db=3.16.0=r42hdfd78af_0
  - bioconductor-org.sc.sgd.db=3.16.0=r42hdfd78af_0
  - bioconductor-plotgardener=1.4.1=r42hb890f52_0
  - bioconductor-plyranges=1.18.0=r42hdfd78af_0
  - bioconductor-protgenerics=1.30.0=r42hdfd78af_0
  - bioconductor-qvalue=2.30.0=r42hdfd78af_0
  - bioconductor-residualmatrix=1.8.0=r42hdfd78af_0
  - bioconductor-rhdf5=2.42.0=r42h01c935f_1
  - bioconductor-rhdf5filters=1.10.0=r42hb890f52_0
  - bioconductor-rhdf5lib=1.20.0=r42h3be46a4_0
  - bioconductor-rhtslib=2.0.0=r42h3be46a4_0
  - bioconductor-rsamtools=2.14.0=r42hb890f52_0
  - bioconductor-rtracklayer=1.58.0=r42h6e925bd_1
  - bioconductor-s4vectors=0.36.0=r42h3be46a4_0
  - bioconductor-scaledmatrix=1.6.0=r42hdfd78af_0
  - bioconductor-scater=1.26.0=r42hdfd78af_0
  - bioconductor-scmerge=1.14.0=r42hdfd78af_0
  - bioconductor-scran=1.26.0=r42hb890f52_0
  - bioconductor-scuttle=1.8.0=r42hb890f52_0
  - bioconductor-seqlogo=1.64.0=r42hdfd78af_0
  - bioconductor-singlecellexperiment=1.20.0=r42hdfd78af_0
  - bioconductor-singler=2.0.0=r42hb890f52_0
  - bioconductor-sparsematrixstats=1.10.0=r42hb890f52_0
  - bioconductor-summarizedexperiment=1.28.0=r42hdfd78af_0
  - bioconductor-tfbstools=1.36.0=r42h3be46a4_0
  - bioconductor-tidysinglecellexperiment=1.8.0=r42hdfd78af_0
  - bioconductor-treeio=1.22.0=r42hdfd78af_0
  - bioconductor-txdb.hsapiens.ucsc.hg38.knowngene=3.16.0=r42hdfd78af_0
  - bioconductor-variantannotation=1.44.0=r42h3be46a4_0
  - bioconductor-xvector=0.38.0=r42h3be46a4_0
  - bioconductor-zlibbioc=1.44.0=r42h3be46a4_0
  - blas=2.116=openblas
  - blas-devel=3.9.0=16_osx64_openblas
  - bwidget=1.9.14=h694c41f_1
  - bzip2=1.0.8=h0d85af4_4
  - c-ares=1.18.1=h0d85af4_0
  - ca-certificates=2023.5.7=h8857fd0_0
  - cairo=1.16.0=h904041c_1014
  - cctools_osx-64=973.0.1=h3eff9a4_10
  - clang=14.0.6=h694c41f_0
  - clang-14=14.0.6=default_h55ffa42_0
  - clang_osx-64=14.0.6=h3113cd8_3
  - clangxx=14.0.6=default_h55ffa42_0
  - clangxx_osx-64=14.0.6=h6f97653_3
  - compiler-rt=14.0.6=h613da45_0
  - compiler-rt_osx-64=14.0.6=hab78ec2_0
  - curl=7.86.0=h57eb407_1
  - expat=2.5.0=hf0c8a7f_0
  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
  - font-ttf-inconsolata=3.000=h77eed37_0
  - font-ttf-source-code-pro=2.038=h77eed37_0
  - font-ttf-ubuntu=0.83=hab24e00_0
  - fontconfig=2.14.1=h5bb23bf_0
  - fonts-conda-ecosystem=1=0
  - fonts-conda-forge=1=0
  - freetype=2.12.1=h3f81eb7_0
  - fribidi=1.0.10=hbcb3906_0
  - geos=3.11.2=hf0c8a7f_0
  - gettext=0.21.1=h8a4c099_0
  - gfortran_impl_osx-64=11.3.0=h1f927f5_26
  - gfortran_osx-64=11.3.0=h18f7dce_0
  - git=2.39.1=pl5321h00ebd2c_0
  - glpk=5.0=h3cb5acd_0
  - gmp=6.2.1=h2e338ed_0
  - graphite2=1.3.13=h2e338ed_1001
  - gsl=2.7=h93259b0_0
  - harfbuzz=5.3.0=h08f8713_0
  - icu=70.1=h96cf925_0
  - importlib-metadata=3.3.0=pyhd8ed1ab_1
  - importlib_metadata=3.3.0=hd8ed1ab_3
  - isl=0.25=hb486fe8_0
  - jpeg=9e=hac89ed1_2
  - jq=1.6=hc929b4f_1000
  - krb5=1.19.3=hb49756b_0
  - ld64_osx-64=609=h1e06c2b_10
  - lerc=4.0.0=hb486fe8_0
  - libblas=3.9.0=16_osx64_openblas
  - libcblas=3.9.0=16_osx64_openblas
  - libclang-cpp14=14.0.6=default_h55ffa42_0
  - libcurl=7.86.0=h57eb407_1
  - libcxx=14.0.6=hccf4f1f_0
  - libdeflate=1.14=hb7f2c08_0
  - libedit=3.1.20191231=h0678c8f_2
  - libev=4.33=haf1e3a3_1
  - libffi=3.4.2=h0d85af4_5
  - libgfortran=5.0.0=9_5_0_h97931a8_26
  - libgfortran-devel_osx-64=11.3.0=h824d247_26
  - libgfortran5=11.3.0=h082f757_26
  - libgit2=1.5.1=h7208b71_0
  - libglib=2.74.1=h4c723e1_1
  - libiconv=1.17=hac89ed1_0
  - liblapack=3.9.0=16_osx64_openblas
  - liblapacke=3.9.0=16_osx64_openblas
  - libllvm13=13.0.1=h64f94b2_2
  - libllvm14=14.0.6=h5b596cc_1
  - libnghttp2=1.47.0=h7cbc4dc_1
  - libopenblas=0.3.21=openmp_h429af6e_3
  - libpng=1.6.39=ha978bb4_0
  - libsqlite=3.39.4=ha978bb4_0
  - libssh2=1.10.0=h7535e13_3
  - libtiff=4.5.0=h6268bbc_0
  - libv8=8.9.83=h20b7c8e_2
  - libwebp-base=1.2.4=h775f41a_0
  - libxcb=1.13=h0d85af4_1004
  - libxml2=2.10.3=hb9e07b5_0
  - libzlib=1.2.13=hfd90126_4
  - llvm-openmp=15.0.4=h61d9ccf_0
  - llvm-tools=14.0.6=h5b596cc_1
  - make=4.3=h22f3db7_1
  - mkl=2022.1.0=h860c996_928
  - mkl-devel=2022.1.0=h694c41f_929
  - mkl-include=2022.1.0=h6bab518_928
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  - pip=22.3.1=pyhd8ed1ab_0
  - pixman=0.40.0=hbcb3906_0
  - pthread-stubs=0.4=hc929b4f_1001
  - python=3.11.0=ha621ccb_0_cpython
  - python_abi=3.11=2_cp311
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